The blog is about 20 days old and as part of the blog entries, I would like to share some OR websites from time to time. These will be tagged as “Useful OR websites”.

The first one of these entries is… the Scheduling Zoo. I was looking for the computational complexity of different scheduling problems for my research and came across this website. The Scheduling Zoo is a searchable bibliography on the complexity of scheduling problems by Peter Brucker and Sigrid Knust and the website is maintained by Christoph Duerr.

At the zoo, you pick your environment (e.g., single machine, job shop, etc.), then, the problem characteristics (e.g., precedence constraints or not) and the objective function (e.g., minimize makespan, minimize the number of late jobs, etc.) then it returns the known complexity results along with the related problems, their complexity and references for each! Given the elephantine literature on scheduling it is a pretty neat, specialized bibliography search website. You can find a related website here.

They collect statistics on the searches. Turns out:

* The most popular objective function is to minimize makespan (1936 searches when I looked) followed by a distant second, to minimize the sum of completion times (517 searches).

* The most popular machine environment is single machine scheduling (1362 searches) followed by parallel machine scheduling with identical machines (665)

* And, the two most popular variants are “release times” (1505 searches) and “precedence constraints” (1038).

This semester I am teaching Linear Programming (LP). This year, we have students from many different backgrounds and departments taking the class, which I think is great news for our field. The more we make OR accessible to other fields, the better. Anyway, this year I decided to do something different and wanted to show the students all the different uses of linear programming in real life. So, I searched INFORMS journals + more for applications of LP. Even though many real-world problems involve nonlinearities and integer decision variables, the list of applications of LP turned out to be pretty impressive. Take a look:

∙ This is an application in alternative energy (specifically wind energy). I came across this while reading GreenOR. Here’s the original entry on it. This application has been developed by the US National Renewable Energy Laboratory (NREL) to determine the expansion of wind electric generation and transmission capacity. It passes information from a Geographic Information System (GIS) into a linear program.

Now that the election results (or, estimates) are in, I was curious to see how the election prediction model of Sheldon H. Jacobson along with co-authors Steven E. Rigdon, Edward C. Sewell and Christopher J. Rigdon has performed. OR bloggers Micheal Trick and Laura McLay wrote about this in their blogs earlier. If you are not familiar with it, here is a link to their website and here is a link to their paper that explains the model. From their paper:

It uses a Bayesian estimation approach that incorporates polling data, including the effect of third party candidates and undecided voters, as input to a dynamic programming algorithm … to build the probability distribution of the total number of Electoral College votes for each candidate.

Their results were last updated on Tuesday, Nov 4th, using the latest polling data. Of course, the model is as correct as the polling data that is given as an input to the model. However, take a look at this:

11:45pm AZ time: Obama has 338 and McCain has 159 electoral votes. The prediction model has 338 Safe Electoral Votes for Obama and 157 Safe Electoral Votes for McCain. They define “safe” when they predict the candidate has a 0.85 chance or better for winning. Montana, Missouri, Indiana and N. Carolina still not decided. In the prediction model, these are the states that are not “safe” along with N. Dakota.

12:15am AZ time: Indiana goes Blue… Hmm… Their model tends to Red… (the only time it does not match)

“National Park Service officials will soon embed microchips in Arizona’s signature saguaro cactus plants to deter thieves who dig them up and sell them to landscapers and nurseries. The microchips, which are inserted with a syringe, will help authorities identify stolen plants.”

Apparently, they sell for about $1,000 each. Talking about microchips, there is substantial research on effective use of RFID chips in OR/MS, for instance, for inventory tracking and control.